Overview

Brought to you by YData

Dataset statistics

Number of variables41
Number of observations10000
Missing cells281
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.2 MiB
Average record size in memory336.0 B

Variable types

Numeric28
DateTime2
Categorical9
Text2

Alerts

MOBILENO_AVL_FLAG has constant value "1" Constant
AADHAR_FLAG is highly overall correlated with STATE_ID and 1 other fieldsHigh correlation
ASSET_COST is highly overall correlated with DISBURSED_AMOUNTHigh correlation
DISBURSED_AMOUNT is highly overall correlated with ASSET_COSTHigh correlation
NEW_ACCTS_IN_LAST_SIX_MONTHS is highly overall correlated with PRI_ACTIVE_ACCTS and 4 other fieldsHigh correlation
PERFORM_CNS_SCORE is highly overall correlated with PERFORM_CNS_SCORE_DESCRIPTION and 6 other fieldsHigh correlation
PERFORM_CNS_SCORE_DESCRIPTION is highly overall correlated with PERFORM_CNS_SCOREHigh correlation
PRIMARY_INSTAL_AMT is highly overall correlated with PERFORM_CNS_SCORE and 5 other fieldsHigh correlation
PRI_ACTIVE_ACCTS is highly overall correlated with NEW_ACCTS_IN_LAST_SIX_MONTHS and 6 other fieldsHigh correlation
PRI_CURRENT_BALANCE is highly overall correlated with NEW_ACCTS_IN_LAST_SIX_MONTHS and 6 other fieldsHigh correlation
PRI_DISBURSED_AMOUNT is highly overall correlated with NEW_ACCTS_IN_LAST_SIX_MONTHS and 6 other fieldsHigh correlation
PRI_NO_OF_ACCTS is highly overall correlated with NEW_ACCTS_IN_LAST_SIX_MONTHS and 6 other fieldsHigh correlation
PRI_SANCTIONED_AMOUNT is highly overall correlated with NEW_ACCTS_IN_LAST_SIX_MONTHS and 6 other fieldsHigh correlation
SEC_ACTIVE_ACCTS is highly overall correlated with SEC_CURRENT_BALANCE and 5 other fieldsHigh correlation
SEC_CURRENT_BALANCE is highly overall correlated with SEC_ACTIVE_ACCTS and 5 other fieldsHigh correlation
SEC_DISBURSED_AMOUNT is highly overall correlated with SEC_ACTIVE_ACCTS and 5 other fieldsHigh correlation
SEC_INSTAL_AMT is highly overall correlated with SEC_ACTIVE_ACCTS and 4 other fieldsHigh correlation
SEC_NO_OF_ACCTS is highly overall correlated with SEC_ACTIVE_ACCTS and 5 other fieldsHigh correlation
SEC_OVERDUE_ACCTS is highly overall correlated with SEC_ACTIVE_ACCTS and 4 other fieldsHigh correlation
SEC_SANCTIONED_AMOUNT is highly overall correlated with SEC_ACTIVE_ACCTS and 5 other fieldsHigh correlation
STATE_ID is highly overall correlated with AADHAR_FLAG and 1 other fieldsHigh correlation
VOTERID_FLAG is highly overall correlated with AADHAR_FLAG and 1 other fieldsHigh correlation
PAN_FLAG is highly imbalanced (61.8%) Imbalance
DRIVING_FLAG is highly imbalanced (84.6%) Imbalance
PASSPORT_FLAG is highly imbalanced (97.5%) Imbalance
EMPLOYMENT_TYPE has 281 (2.8%) missing values Missing
SEC_NO_OF_ACCTS is highly skewed (γ1 = 28.48033388) Skewed
SEC_ACTIVE_ACCTS is highly skewed (γ1 = 22.40878643) Skewed
SEC_OVERDUE_ACCTS is highly skewed (γ1 = 23.86177536) Skewed
SEC_CURRENT_BALANCE is highly skewed (γ1 = 46.91407737) Skewed
SEC_SANCTIONED_AMOUNT is highly skewed (γ1 = 42.77652496) Skewed
SEC_DISBURSED_AMOUNT is highly skewed (γ1 = 42.90236866) Skewed
PRIMARY_INSTAL_AMT is highly skewed (γ1 = 29.58056146) Skewed
SEC_INSTAL_AMT is highly skewed (γ1 = 34.50502137) Skewed
UNIQUEID has unique values Unique
PERFORM_CNS_SCORE has 4954 (49.5%) zeros Zeros
PRI_NO_OF_ACCTS has 4954 (49.5%) zeros Zeros
PRI_ACTIVE_ACCTS has 5804 (58.0%) zeros Zeros
PRI_OVERDUE_ACCTS has 8832 (88.3%) zeros Zeros
PRI_CURRENT_BALANCE has 6016 (60.2%) zeros Zeros
PRI_SANCTIONED_AMOUNT has 5859 (58.6%) zeros Zeros
PRI_DISBURSED_AMOUNT has 5863 (58.6%) zeros Zeros
SEC_NO_OF_ACCTS has 9738 (97.4%) zeros Zeros
SEC_ACTIVE_ACCTS has 9822 (98.2%) zeros Zeros
SEC_OVERDUE_ACCTS has 9927 (99.3%) zeros Zeros
SEC_CURRENT_BALANCE has 9850 (98.5%) zeros Zeros
SEC_SANCTIONED_AMOUNT has 9824 (98.2%) zeros Zeros
SEC_DISBURSED_AMOUNT has 9826 (98.3%) zeros Zeros
PRIMARY_INSTAL_AMT has 6776 (67.8%) zeros Zeros
SEC_INSTAL_AMT has 9895 (99.0%) zeros Zeros
NEW_ACCTS_IN_LAST_SIX_MONTHS has 7737 (77.4%) zeros Zeros
DELINQUENT_ACCTS_IN_LAST_SIX_MONTHS has 9182 (91.8%) zeros Zeros
NO_OF_INQUIRIES has 8624 (86.2%) zeros Zeros

Reproduction

Analysis started2025-03-05 17:44:13.360812
Analysis finished2025-03-05 17:50:03.138822
Duration5 minutes and 49.78 seconds
Software versionydata-profiling vv4.13.0
Download configurationconfig.json

Variables

UNIQUEID
Real number (ℝ)

Unique 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean534957.38
Minimum417465
Maximum658669
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.2 KiB
2025-03-05T20:50:03.565116image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum417465
5-th percentile427691.5
Q1475017.75
median535230.5
Q3595294.5
95-th percentile642466.35
Maximum658669
Range241204
Interquartile range (IQR)120276.75

Descriptive statistics

Standard deviation68956.515
Coefficient of variation (CV)0.12890095
Kurtosis-1.2152276
Mean534957.38
Median Absolute Deviation (MAD)60098.5
Skewness0.0054766771
Sum5.3495738 × 109
Variance4.7550009 × 109
MonotonicityNot monotonic
2025-03-05T20:50:04.211858image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
573637 1
 
< 0.1%
603047 1
 
< 0.1%
575247 1
 
< 0.1%
515504 1
 
< 0.1%
576058 1
 
< 0.1%
506023 1
 
< 0.1%
589170 1
 
< 0.1%
544296 1
 
< 0.1%
421254 1
 
< 0.1%
427094 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
417465 1
< 0.1%
417529 1
< 0.1%
417583 1
< 0.1%
417586 1
< 0.1%
417587 1
< 0.1%
417637 1
< 0.1%
417650 1
< 0.1%
417669 1
< 0.1%
417674 1
< 0.1%
417684 1
< 0.1%
ValueCountFrequency (%)
658669 1
< 0.1%
658658 1
< 0.1%
658653 1
< 0.1%
654066 1
< 0.1%
654009 1
< 0.1%
653962 1
< 0.1%
653954 1
< 0.1%
653950 1
< 0.1%
653943 1
< 0.1%
653934 1
< 0.1%

DISBURSED_AMOUNT
Real number (ℝ)

High correlation 

Distinct3447
Distinct (%)34.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54271.672
Minimum13664
Maximum153318
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.2 KiB
2025-03-05T20:50:04.870788image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum13664
5-th percentile35362.1
Q147153.5
median53728.5
Q360263
95-th percentile73717
Maximum153318
Range139654
Interquartile range (IQR)13109.5

Descriptive statistics

Standard deviation12304.739
Coefficient of variation (CV)0.22672489
Kurtosis3.205154
Mean54271.672
Median Absolute Deviation (MAD)6564.5
Skewness0.79422149
Sum5.4271672 × 108
Variance1.514066 × 108
MonotonicityNot monotonic
2025-03-05T20:50:05.427142image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
48349 105
 
1.1%
55259 91
 
0.9%
52303 89
 
0.9%
47349 87
 
0.9%
51303 84
 
0.8%
53303 75
 
0.8%
46349 73
 
0.7%
57259 72
 
0.7%
50303 72
 
0.7%
45349 62
 
0.6%
Other values (3437) 9190
91.9%
ValueCountFrequency (%)
13664 2
< 0.1%
14140 1
< 0.1%
14930 1
< 0.1%
15619 1
< 0.1%
15910 1
< 0.1%
16500 1
< 0.1%
16619 1
< 0.1%
17239 1
< 0.1%
17634 1
< 0.1%
17739 1
< 0.1%
ValueCountFrequency (%)
153318 1
< 0.1%
140523 1
< 0.1%
132531 1
< 0.1%
130499 1
< 0.1%
129598 1
< 0.1%
127103 1
< 0.1%
123208 1
< 0.1%
121238 1
< 0.1%
118618 1
< 0.1%
118328 1
< 0.1%

ASSET_COST
Real number (ℝ)

High correlation 

Distinct7347
Distinct (%)73.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75604.08
Minimum38055
Maximum247078
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.2 KiB
2025-03-05T20:50:06.394098image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum38055
5-th percentile58411.4
Q165581.75
median70828
Q378987.25
95-th percentile109300.2
Maximum247078
Range209023
Interquartile range (IQR)13405.5

Descriptive statistics

Standard deviation17978.818
Coefficient of variation (CV)0.23780222
Kurtosis8.657291
Mean75604.08
Median Absolute Deviation (MAD)6172
Skewness2.3794765
Sum7.560408 × 108
Variance3.2323789 × 108
MonotonicityNot monotonic
2025-03-05T20:50:07.324198image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
72000 32
 
0.3%
68000 32
 
0.3%
75000 28
 
0.3%
67000 27
 
0.3%
77000 23
 
0.2%
69000 21
 
0.2%
66000 20
 
0.2%
63000 20
 
0.2%
70000 19
 
0.2%
73000 19
 
0.2%
Other values (7337) 9759
97.6%
ValueCountFrequency (%)
38055 1
< 0.1%
38752 1
< 0.1%
39217 1
< 0.1%
39536 1
< 0.1%
40167 1
< 0.1%
40175 2
< 0.1%
40600 1
< 0.1%
40700 1
< 0.1%
40768 1
< 0.1%
40893 1
< 0.1%
ValueCountFrequency (%)
247078 1
< 0.1%
206518 1
< 0.1%
199371 1
< 0.1%
196451 1
< 0.1%
190960 1
< 0.1%
189130 1
< 0.1%
188134 1
< 0.1%
185531 1
< 0.1%
180661 1
< 0.1%
180000 1
< 0.1%

LTV
Real number (ℝ)

Distinct3461
Distinct (%)34.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74.867973
Minimum16.6
Maximum94.99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.2 KiB
2025-03-05T20:50:08.023969image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum16.6
5-th percentile52.998
Q169.125
median76.87
Q383.52
95-th percentile89.34
Maximum94.99
Range78.39
Interquartile range (IQR)14.395

Descriptive statistics

Standard deviation11.241177
Coefficient of variation (CV)0.15014668
Kurtosis1.4445029
Mean74.867973
Median Absolute Deviation (MAD)7.11
Skewness-1.0898454
Sum748679.73
Variance126.36407
MonotonicityNot monotonic
2025-03-05T20:50:08.758946image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
85 208
 
2.1%
84.99 51
 
0.5%
79.99 28
 
0.3%
79.9 23
 
0.2%
84.95 19
 
0.2%
74.93 18
 
0.2%
79.92 18
 
0.2%
74.99 17
 
0.2%
80 17
 
0.2%
84.96 17
 
0.2%
Other values (3451) 9584
95.8%
ValueCountFrequency (%)
16.6 1
< 0.1%
18.08 1
< 0.1%
21.8 1
< 0.1%
22.66 1
< 0.1%
22.76 1
< 0.1%
22.79 1
< 0.1%
23.72 1
< 0.1%
23.76 1
< 0.1%
24.2 1
< 0.1%
26.88 1
< 0.1%
ValueCountFrequency (%)
94.99 1
 
< 0.1%
94.95 1
 
< 0.1%
94.92 3
< 0.1%
94.91 1
 
< 0.1%
94.88 2
< 0.1%
94.85 1
 
< 0.1%
94.82 1
 
< 0.1%
94.81 1
 
< 0.1%
94.8 2
< 0.1%
94.78 1
 
< 0.1%

BRANCH_ID
Real number (ℝ)

Distinct82
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72.8098
Minimum1
Maximum261
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.2 KiB
2025-03-05T20:50:09.777959image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q114
median61
Q3121
95-th percentile249.05
Maximum261
Range260
Interquartile range (IQR)107

Descriptive statistics

Standard deviation70.323407
Coefficient of variation (CV)0.96585085
Kurtosis0.3227596
Mean72.8098
Median Absolute Deviation (MAD)50
Skewness1.0490101
Sum728098
Variance4945.3816
MonotonicityNot monotonic
2025-03-05T20:50:10.470390image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 546
 
5.5%
67 446
 
4.5%
3 413
 
4.1%
5 398
 
4.0%
36 382
 
3.8%
34 375
 
3.8%
136 334
 
3.3%
16 276
 
2.8%
19 246
 
2.5%
18 229
 
2.3%
Other values (72) 6355
63.5%
ValueCountFrequency (%)
1 228
2.3%
2 546
5.5%
3 413
4.1%
5 398
4.0%
7 138
 
1.4%
8 145
 
1.5%
9 102
 
1.0%
10 192
 
1.9%
11 198
 
2.0%
13 137
 
1.4%
ValueCountFrequency (%)
261 9
 
0.1%
260 16
 
0.2%
259 17
 
0.2%
258 18
 
0.2%
257 46
 
0.5%
255 62
 
0.6%
254 70
 
0.7%
251 184
1.8%
250 78
0.8%
249 47
 
0.5%

SUPPLIER_ID
Real number (ℝ)

Distinct1991
Distinct (%)19.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19677.888
Minimum12312
Maximum24793
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.2 KiB
2025-03-05T20:50:10.939673image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum12312
5-th percentile14180
Q116574.75
median20335
Q323029.75
95-th percentile24130
Maximum24793
Range12481
Interquartile range (IQR)6455

Descriptive statistics

Standard deviation3497.8814
Coefficient of variation (CV)0.17775695
Kurtosis-1.4765337
Mean19677.888
Median Absolute Deviation (MAD)3090
Skewness-0.17665164
Sum1.9677888 × 108
Variance12235174
MonotonicityNot monotonic
2025-03-05T20:50:11.431958image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18317 65
 
0.7%
21980 58
 
0.6%
17980 51
 
0.5%
18532 50
 
0.5%
15694 50
 
0.5%
15663 50
 
0.5%
14234 50
 
0.5%
14145 48
 
0.5%
22727 46
 
0.5%
21124 45
 
0.4%
Other values (1981) 9487
94.9%
ValueCountFrequency (%)
12312 2
 
< 0.1%
12374 6
0.1%
12441 5
0.1%
12456 4
< 0.1%
12500 2
 
< 0.1%
12797 3
< 0.1%
12842 1
 
< 0.1%
12878 1
 
< 0.1%
13131 2
 
< 0.1%
13295 1
 
< 0.1%
ValueCountFrequency (%)
24793 1
 
< 0.1%
24777 1
 
< 0.1%
24770 3
< 0.1%
24761 1
 
< 0.1%
24754 1
 
< 0.1%
24745 2
< 0.1%
24744 1
 
< 0.1%
24728 2
< 0.1%
24727 1
 
< 0.1%
24721 1
 
< 0.1%

MANUFACTURER_ID
Real number (ℝ)

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.2577
Minimum45
Maximum145
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.2 KiB
2025-03-05T20:50:11.868605image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum45
5-th percentile45
Q148
median86
Q386
95-th percentile86
Maximum145
Range100
Interquartile range (IQR)38

Descriptive statistics

Standard deviation22.353963
Coefficient of variation (CV)0.32276502
Kurtosis-0.69849295
Mean69.2577
Median Absolute Deviation (MAD)34
Skewness0.39820101
Sum692577
Variance499.69966
MonotonicityNot monotonic
2025-03-05T20:50:12.236700image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
86 4694
46.9%
45 2423
24.2%
51 1128
 
11.3%
48 735
 
7.3%
120 448
 
4.5%
49 440
 
4.4%
67 95
 
0.9%
145 37
 
0.4%
ValueCountFrequency (%)
45 2423
24.2%
48 735
 
7.3%
49 440
 
4.4%
51 1128
 
11.3%
67 95
 
0.9%
86 4694
46.9%
120 448
 
4.5%
145 37
 
0.4%
ValueCountFrequency (%)
145 37
 
0.4%
120 448
 
4.5%
86 4694
46.9%
67 95
 
0.9%
51 1128
 
11.3%
49 440
 
4.4%
48 735
 
7.3%
45 2423
24.2%

CURRENT_PINCODE_ID
Real number (ℝ)

Distinct3141
Distinct (%)31.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3402.1104
Minimum2
Maximum7333
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.2 KiB
2025-03-05T20:50:12.712720image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile231
Q11514
median2964
Q35681
95-th percentile6940.05
Maximum7333
Range7331
Interquartile range (IQR)4167

Descriptive statistics

Standard deviation2240.5176
Coefficient of variation (CV)0.65856699
Kurtosis-1.2869814
Mean3402.1104
Median Absolute Deviation (MAD)1915
Skewness0.27046087
Sum34021104
Variance5019919.2
MonotonicityNot monotonic
2025-03-05T20:50:13.238436image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2578 91
 
0.9%
1446 80
 
0.8%
2989 55
 
0.5%
2943 43
 
0.4%
1515 38
 
0.4%
2378 37
 
0.4%
2790 35
 
0.4%
1794 35
 
0.4%
1509 34
 
0.3%
2782 34
 
0.3%
Other values (3131) 9518
95.2%
ValueCountFrequency (%)
2 3
 
< 0.1%
3 1
 
< 0.1%
4 5
 
0.1%
5 13
0.1%
6 5
 
0.1%
7 7
0.1%
9 1
 
< 0.1%
11 1
 
< 0.1%
12 1
 
< 0.1%
13 1
 
< 0.1%
ValueCountFrequency (%)
7333 2
< 0.1%
7331 2
< 0.1%
7328 1
< 0.1%
7324 1
< 0.1%
7321 1
< 0.1%
7315 2
< 0.1%
7311 1
< 0.1%
7308 1
< 0.1%
7304 1
< 0.1%
7302 2
< 0.1%
Distinct5213
Distinct (%)52.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum1949-09-15 00:00:00
Maximum2000-09-24 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-03-05T20:50:13.763875image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:50:14.261644image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

EMPLOYMENT_TYPE
Categorical

Missing 

Distinct2
Distinct (%)< 0.1%
Missing281
Missing (%)2.8%
Memory size156.2 KiB
Self employed
5512 
Salaried
4207 

Length

Max length13
Median length13
Mean length10.835683
Min length8

Characters and Unicode

Total characters105312
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSalaried
2nd rowSelf employed
3rd rowSalaried
4th rowSelf employed
5th rowSelf employed

Common Values

ValueCountFrequency (%)
Self employed 5512
55.1%
Salaried 4207
42.1%
(Missing) 281
 
2.8%

Length

2025-03-05T20:50:14.985852image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-05T20:50:15.352657image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
self 5512
36.2%
employed 5512
36.2%
salaried 4207
27.6%

Most occurring characters

ValueCountFrequency (%)
e 20743
19.7%
l 15231
14.5%
S 9719
9.2%
d 9719
9.2%
a 8414
8.0%
f 5512
 
5.2%
5512
 
5.2%
m 5512
 
5.2%
p 5512
 
5.2%
o 5512
 
5.2%
Other values (3) 13926
13.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 105312
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 20743
19.7%
l 15231
14.5%
S 9719
9.2%
d 9719
9.2%
a 8414
8.0%
f 5512
 
5.2%
5512
 
5.2%
m 5512
 
5.2%
p 5512
 
5.2%
o 5512
 
5.2%
Other values (3) 13926
13.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 105312
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 20743
19.7%
l 15231
14.5%
S 9719
9.2%
d 9719
9.2%
a 8414
8.0%
f 5512
 
5.2%
5512
 
5.2%
m 5512
 
5.2%
p 5512
 
5.2%
o 5512
 
5.2%
Other values (3) 13926
13.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 105312
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 20743
19.7%
l 15231
14.5%
S 9719
9.2%
d 9719
9.2%
a 8414
8.0%
f 5512
 
5.2%
5512
 
5.2%
m 5512
 
5.2%
p 5512
 
5.2%
o 5512
 
5.2%
Other values (3) 13926
13.2%
Distinct83
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2018-08-01 00:00:00
Maximum2018-10-31 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-03-05T20:50:16.058689image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:50:17.388622image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

STATE_ID
Real number (ℝ)

High correlation 

Distinct22
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.2421
Minimum1
Maximum22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.2 KiB
2025-03-05T20:50:18.134343image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14
median6
Q310
95-th percentile16
Maximum22
Range21
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.4631468
Coefficient of variation (CV)0.61627799
Kurtosis-0.33005249
Mean7.2421
Median Absolute Deviation (MAD)3
Skewness0.81712538
Sum72421
Variance19.91968
MonotonicityNot monotonic
2025-03-05T20:50:18.679711image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
4 1960
19.6%
3 1449
14.5%
6 1404
14.0%
13 776
 
7.8%
9 680
 
6.8%
8 628
 
6.3%
5 440
 
4.4%
14 400
 
4.0%
1 389
 
3.9%
11 300
 
3.0%
Other values (12) 1574
15.7%
ValueCountFrequency (%)
1 389
 
3.9%
2 179
 
1.8%
3 1449
14.5%
4 1960
19.6%
5 440
 
4.4%
6 1404
14.0%
7 281
 
2.8%
8 628
 
6.3%
9 680
 
6.8%
10 145
 
1.5%
ValueCountFrequency (%)
22 2
 
< 0.1%
21 9
 
0.1%
20 10
 
0.1%
19 45
 
0.4%
18 210
 
2.1%
17 159
 
1.6%
16 127
 
1.3%
15 209
 
2.1%
14 400
4.0%
13 776
7.8%

EMPLOYEE_CODE_ID
Real number (ℝ)

Distinct2645
Distinct (%)26.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1569.318
Minimum1
Maximum3775
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.2 KiB
2025-03-05T20:50:19.078443image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile157.9
Q1740
median1478
Q32393
95-th percentile3188.05
Maximum3775
Range3774
Interquartile range (IQR)1653

Descriptive statistics

Standard deviation972.71283
Coefficient of variation (CV)0.61983156
Kurtosis-1.0732914
Mean1569.318
Median Absolute Deviation (MAD)807
Skewness0.21501101
Sum15693180
Variance946170.25
MonotonicityNot monotonic
2025-03-05T20:50:19.431602image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
255 31
 
0.3%
2546 31
 
0.3%
2153 22
 
0.2%
845 21
 
0.2%
620 21
 
0.2%
2601 19
 
0.2%
628 18
 
0.2%
1831 18
 
0.2%
1439 17
 
0.2%
1946 17
 
0.2%
Other values (2635) 9785
97.9%
ValueCountFrequency (%)
1 4
< 0.1%
3 8
0.1%
4 2
 
< 0.1%
5 4
< 0.1%
7 4
< 0.1%
9 2
 
< 0.1%
10 3
 
< 0.1%
11 6
0.1%
12 4
< 0.1%
15 2
 
< 0.1%
ValueCountFrequency (%)
3775 1
< 0.1%
3765 1
< 0.1%
3760 1
< 0.1%
3759 1
< 0.1%
3753 1
< 0.1%
3740 1
< 0.1%
3725 2
< 0.1%
3723 1
< 0.1%
3721 1
< 0.1%
3720 1
< 0.1%

MOBILENO_AVL_FLAG
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
10000 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 10000
100.0%

Length

2025-03-05T20:50:19.716608image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-05T20:50:20.346734image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 10000
100.0%

Most occurring characters

ValueCountFrequency (%)
1 10000
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 10000
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 10000
100.0%

AADHAR_FLAG
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
8402 
0
1598 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 8402
84.0%
0 1598
 
16.0%

Length

2025-03-05T20:50:20.794670image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-05T20:50:21.324576image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 8402
84.0%
0 1598
 
16.0%

Most occurring characters

ValueCountFrequency (%)
1 8402
84.0%
0 1598
 
16.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 8402
84.0%
0 1598
 
16.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 8402
84.0%
0 1598
 
16.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 8402
84.0%
0 1598
 
16.0%

PAN_FLAG
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
9255 
1
 
745

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 9255
92.5%
1 745
 
7.4%

Length

2025-03-05T20:50:21.664231image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-05T20:50:21.969963image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 9255
92.5%
1 745
 
7.4%

Most occurring characters

ValueCountFrequency (%)
0 9255
92.5%
1 745
 
7.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 9255
92.5%
1 745
 
7.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 9255
92.5%
1 745
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 9255
92.5%
1 745
 
7.4%

VOTERID_FLAG
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
8552 
1
1448 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 8552
85.5%
1 1448
 
14.5%

Length

2025-03-05T20:50:22.173640image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-05T20:50:22.372314image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 8552
85.5%
1 1448
 
14.5%

Most occurring characters

ValueCountFrequency (%)
0 8552
85.5%
1 1448
 
14.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 8552
85.5%
1 1448
 
14.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 8552
85.5%
1 1448
 
14.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 8552
85.5%
1 1448
 
14.5%

DRIVING_FLAG
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
9777 
1
 
223

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 9777
97.8%
1 223
 
2.2%

Length

2025-03-05T20:50:22.637510image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-05T20:50:22.990374image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 9777
97.8%
1 223
 
2.2%

Most occurring characters

ValueCountFrequency (%)
0 9777
97.8%
1 223
 
2.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 9777
97.8%
1 223
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 9777
97.8%
1 223
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 9777
97.8%
1 223
 
2.2%

PASSPORT_FLAG
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
9975 
1
 
25

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 9975
99.8%
1 25
 
0.2%

Length

2025-03-05T20:50:23.253837image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-05T20:50:23.454222image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 9975
99.8%
1 25
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 9975
99.8%
1 25
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 9975
99.8%
1 25
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 9975
99.8%
1 25
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 9975
99.8%
1 25
 
0.2%

PERFORM_CNS_SCORE
Real number (ℝ)

High correlation  Zeros 

Distinct464
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean293.6837
Minimum0
Maximum879
Zeros4954
Zeros (%)49.5%
Negative0
Negative (%)0.0%
Memory size156.2 KiB
2025-03-05T20:50:23.718113image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median15
Q3679
95-th percentile825
Maximum879
Range879
Interquartile range (IQR)679

Descriptive statistics

Standard deviation338.67789
Coefficient of variation (CV)1.1532063
Kurtosis-1.6567695
Mean293.6837
Median Absolute Deviation (MAD)15
Skewness0.41831979
Sum2936837
Variance114702.71
MonotonicityNot monotonic
2025-03-05T20:50:24.243062image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4954
49.5%
738 364
 
3.6%
300 362
 
3.6%
825 320
 
3.2%
15 170
 
1.7%
763 155
 
1.6%
17 154
 
1.5%
16 126
 
1.3%
708 102
 
1.0%
737 95
 
0.9%
Other values (454) 3198
32.0%
ValueCountFrequency (%)
0 4954
49.5%
14 30
 
0.3%
15 170
 
1.7%
16 126
 
1.3%
17 154
 
1.5%
18 65
 
0.7%
300 362
 
3.6%
302 1
 
< 0.1%
305 6
 
0.1%
306 2
 
< 0.1%
ValueCountFrequency (%)
879 2
 
< 0.1%
873 1
 
< 0.1%
870 1
 
< 0.1%
858 1
 
< 0.1%
853 4
 
< 0.1%
845 23
0.2%
844 3
 
< 0.1%
842 1
 
< 0.1%
841 1
 
< 0.1%
839 6
 
0.1%

PERFORM_CNS_SCORE_DESCRIPTION
Categorical

High correlation 

Distinct19
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
No Bureau History Available
4954 
C-Very Low Risk
682 
A-Very Low Risk
602 
D-Very Low Risk
 
483
B-Very Low Risk
 
424
Other values (14)
2855 

Length

Max length55
Median length53
Mean length22.1101
Min length10

Characters and Unicode

Total characters221101
Distinct characters48
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo Bureau History Available
2nd rowNo Bureau History Available
3rd rowB-Very Low Risk
4th rowNo Bureau History Available
5th rowNo Bureau History Available

Common Values

ValueCountFrequency (%)
No Bureau History Available 4954
49.5%
C-Very Low Risk 682
 
6.8%
A-Very Low Risk 602
 
6.0%
D-Very Low Risk 483
 
4.8%
B-Very Low Risk 424
 
4.2%
K-High Risk 402
 
4.0%
F-Low Risk 374
 
3.7%
M-Very High Risk 362
 
3.6%
H-Medium Risk 317
 
3.2%
I-Medium Risk 256
 
2.6%
Other values (9) 1144
 
11.4%

Length

2025-03-05T20:50:24.501242image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
available 5343
15.0%
no 5145
14.4%
history 5124
14.4%
bureau 4954
13.9%
risk 4501
12.6%
low 2191
 
6.1%
not 869
 
2.4%
c-very 682
 
1.9%
a-very 602
 
1.7%
scored 545
 
1.5%
Other values (29) 5737
16.1%

Most occurring characters

ValueCountFrequency (%)
25693
 
11.6%
i 17276
 
7.8%
a 16205
 
7.3%
o 15622
 
7.1%
e 15188
 
6.9%
r 13563
 
6.1%
u 11115
 
5.0%
l 10781
 
4.9%
s 10226
 
4.6%
y 7880
 
3.6%
Other values (38) 77552
35.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 221101
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
25693
 
11.6%
i 17276
 
7.8%
a 16205
 
7.3%
o 15622
 
7.1%
e 15188
 
6.9%
r 13563
 
6.1%
u 11115
 
5.0%
l 10781
 
4.9%
s 10226
 
4.6%
y 7880
 
3.6%
Other values (38) 77552
35.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 221101
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
25693
 
11.6%
i 17276
 
7.8%
a 16205
 
7.3%
o 15622
 
7.1%
e 15188
 
6.9%
r 13563
 
6.1%
u 11115
 
5.0%
l 10781
 
4.9%
s 10226
 
4.6%
y 7880
 
3.6%
Other values (38) 77552
35.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 221101
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
25693
 
11.6%
i 17276
 
7.8%
a 16205
 
7.3%
o 15622
 
7.1%
e 15188
 
6.9%
r 13563
 
6.1%
u 11115
 
5.0%
l 10781
 
4.9%
s 10226
 
4.6%
y 7880
 
3.6%
Other values (38) 77552
35.1%

PRI_NO_OF_ACCTS
Real number (ℝ)

High correlation  Zeros 

Distinct54
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4961
Minimum0
Maximum99
Zeros4954
Zeros (%)49.5%
Negative0
Negative (%)0.0%
Memory size156.2 KiB
2025-03-05T20:50:24.756186image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile11
Maximum99
Range99
Interquartile range (IQR)3

Descriptive statistics

Standard deviation5.088455
Coefficient of variation (CV)2.0385621
Kurtosis42.183176
Mean2.4961
Median Absolute Deviation (MAD)1
Skewness4.9019195
Sum24961
Variance25.892374
MonotonicityNot monotonic
2025-03-05T20:50:25.147369image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4954
49.5%
1 1512
 
15.1%
2 865
 
8.6%
3 529
 
5.3%
4 405
 
4.0%
5 319
 
3.2%
6 257
 
2.6%
7 207
 
2.1%
8 151
 
1.5%
9 131
 
1.3%
Other values (44) 670
 
6.7%
ValueCountFrequency (%)
0 4954
49.5%
1 1512
 
15.1%
2 865
 
8.6%
3 529
 
5.3%
4 405
 
4.0%
5 319
 
3.2%
6 257
 
2.6%
7 207
 
2.1%
8 151
 
1.5%
9 131
 
1.3%
ValueCountFrequency (%)
99 1
 
< 0.1%
76 1
 
< 0.1%
68 1
 
< 0.1%
67 1
 
< 0.1%
65 1
 
< 0.1%
57 2
< 0.1%
55 1
 
< 0.1%
52 1
 
< 0.1%
49 1
 
< 0.1%
46 3
< 0.1%

PRI_ACTIVE_ACCTS
Real number (ℝ)

High correlation  Zeros 

Distinct25
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0669
Minimum0
Maximum34
Zeros5804
Zeros (%)58.0%
Negative0
Negative (%)0.0%
Memory size156.2 KiB
2025-03-05T20:50:25.432335image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile5
Maximum34
Range34
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.0132634
Coefficient of variation (CV)1.8870217
Kurtosis28.954828
Mean1.0669
Median Absolute Deviation (MAD)0
Skewness4.0186955
Sum10669
Variance4.0532297
MonotonicityNot monotonic
2025-03-05T20:50:25.714997image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0 5804
58.0%
1 1889
 
18.9%
2 911
 
9.1%
3 507
 
5.1%
4 308
 
3.1%
5 215
 
2.1%
6 114
 
1.1%
7 85
 
0.9%
8 44
 
0.4%
9 33
 
0.3%
Other values (15) 90
 
0.9%
ValueCountFrequency (%)
0 5804
58.0%
1 1889
 
18.9%
2 911
 
9.1%
3 507
 
5.1%
4 308
 
3.1%
5 215
 
2.1%
6 114
 
1.1%
7 85
 
0.9%
8 44
 
0.4%
9 33
 
0.3%
ValueCountFrequency (%)
34 1
 
< 0.1%
28 2
 
< 0.1%
24 1
 
< 0.1%
23 1
 
< 0.1%
20 2
 
< 0.1%
19 2
 
< 0.1%
18 1
 
< 0.1%
17 2
 
< 0.1%
16 7
0.1%
15 4
< 0.1%

PRI_OVERDUE_ACCTS
Real number (ℝ)

Zeros 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1626
Minimum0
Maximum23
Zeros8832
Zeros (%)88.3%
Negative0
Negative (%)0.0%
Memory size156.2 KiB
2025-03-05T20:50:26.025970image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum23
Range23
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.59631938
Coefficient of variation (CV)3.6674008
Kurtosis312.1683
Mean0.1626
Median Absolute Deviation (MAD)0
Skewness11.708615
Sum1626
Variance0.3555968
MonotonicityNot monotonic
2025-03-05T20:50:26.300802image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 8832
88.3%
1 892
 
8.9%
2 193
 
1.9%
3 48
 
0.5%
4 20
 
0.2%
6 5
 
0.1%
5 4
 
< 0.1%
7 2
 
< 0.1%
17 1
 
< 0.1%
8 1
 
< 0.1%
Other values (2) 2
 
< 0.1%
ValueCountFrequency (%)
0 8832
88.3%
1 892
 
8.9%
2 193
 
1.9%
3 48
 
0.5%
4 20
 
0.2%
5 4
 
< 0.1%
6 5
 
0.1%
7 2
 
< 0.1%
8 1
 
< 0.1%
12 1
 
< 0.1%
ValueCountFrequency (%)
23 1
 
< 0.1%
17 1
 
< 0.1%
12 1
 
< 0.1%
8 1
 
< 0.1%
7 2
 
< 0.1%
6 5
 
0.1%
5 4
 
< 0.1%
4 20
 
0.2%
3 48
 
0.5%
2 193
1.9%

PRI_CURRENT_BALANCE
Real number (ℝ)

High correlation  Zeros 

Distinct3827
Distinct (%)38.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean180406.26
Minimum-33286
Maximum36939084
Zeros6016
Zeros (%)60.2%
Negative18
Negative (%)0.2%
Memory size156.2 KiB
2025-03-05T20:50:26.562937image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-33286
5-th percentile0
Q10
median0
Q335437.25
95-th percentile862995.75
Maximum36939084
Range36972370
Interquartile range (IQR)35437.25

Descriptive statistics

Standard deviation1070291.3
Coefficient of variation (CV)5.9326728
Kurtosis527.29183
Mean180406.26
Median Absolute Deviation (MAD)0
Skewness19.907179
Sum1.8040626 × 109
Variance1.1455235 × 1012
MonotonicityNot monotonic
2025-03-05T20:50:26.873835image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6016
60.2%
5000 7
 
0.1%
800 6
 
0.1%
30000 5
 
0.1%
22000 4
 
< 0.1%
20000 4
 
< 0.1%
59000 4
 
< 0.1%
1600 4
 
< 0.1%
40000 3
 
< 0.1%
25000 3
 
< 0.1%
Other values (3817) 3944
39.4%
ValueCountFrequency (%)
-33286 1
< 0.1%
-15522 1
< 0.1%
-7114 1
< 0.1%
-6711 1
< 0.1%
-2989 1
< 0.1%
-826 1
< 0.1%
-311 1
< 0.1%
-214 1
< 0.1%
-201 1
< 0.1%
-52 1
< 0.1%
ValueCountFrequency (%)
36939084 1
< 0.1%
33945092 1
< 0.1%
32027420 1
< 0.1%
28990920 1
< 0.1%
28199256 1
< 0.1%
27128894 1
< 0.1%
24705112 1
< 0.1%
21575590 1
< 0.1%
18244196 1
< 0.1%
17216416 1
< 0.1%

PRI_SANCTIONED_AMOUNT
Real number (ℝ)

High correlation  Zeros 

Distinct2805
Distinct (%)28.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean231450.06
Minimum0
Maximum42868148
Zeros5859
Zeros (%)58.6%
Negative0
Negative (%)0.0%
Memory size156.2 KiB
2025-03-05T20:50:27.175244image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q363834.25
95-th percentile1100121.3
Maximum42868148
Range42868148
Interquartile range (IQR)63834.25

Descriptive statistics

Standard deviation1286267.4
Coefficient of variation (CV)5.5574296
Kurtosis479.84007
Mean231450.06
Median Absolute Deviation (MAD)0
Skewness18.982188
Sum2.3145006 × 109
Variance1.6544839 × 1012
MonotonicityNot monotonic
2025-03-05T20:50:27.555940image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5859
58.6%
50000 67
 
0.7%
30000 64
 
0.6%
100000 50
 
0.5%
20000 46
 
0.5%
40000 42
 
0.4%
25000 39
 
0.4%
200000 25
 
0.2%
60000 23
 
0.2%
300000 23
 
0.2%
Other values (2795) 3762
37.6%
ValueCountFrequency (%)
0 5859
58.6%
2 2
 
< 0.1%
4 2
 
< 0.1%
5 2
 
< 0.1%
6 3
 
< 0.1%
8 1
 
< 0.1%
10 1
 
< 0.1%
12 1
 
< 0.1%
15 1
 
< 0.1%
16 1
 
< 0.1%
ValueCountFrequency (%)
42868148 1
< 0.1%
38851416 1
< 0.1%
38092404 1
< 0.1%
37630424 1
< 0.1%
29354052 1
< 0.1%
28893496 1
< 0.1%
27219772 1
< 0.1%
25510316 1
< 0.1%
25118900 1
< 0.1%
23306064 1
< 0.1%

PRI_DISBURSED_AMOUNT
Real number (ℝ)

High correlation  Zeros 

Distinct2924
Distinct (%)29.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean230204.95
Minimum0
Maximum42868148
Zeros5863
Zeros (%)58.6%
Negative0
Negative (%)0.0%
Memory size156.2 KiB
2025-03-05T20:50:27.962024image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q362033.25
95-th percentile1095250
Maximum42868148
Range42868148
Interquartile range (IQR)62033.25

Descriptive statistics

Standard deviation1284395.9
Coefficient of variation (CV)5.5793584
Kurtosis481.03444
Mean230204.95
Median Absolute Deviation (MAD)0
Skewness19.017546
Sum2.3020495 × 109
Variance1.649673 × 1012
MonotonicityNot monotonic
2025-03-05T20:50:28.587444image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5863
58.6%
50000 66
 
0.7%
30000 59
 
0.6%
100000 48
 
0.5%
40000 39
 
0.4%
20000 34
 
0.3%
25000 29
 
0.3%
300000 24
 
0.2%
200000 24
 
0.2%
45000 22
 
0.2%
Other values (2914) 3792
37.9%
ValueCountFrequency (%)
0 5863
58.6%
2 2
 
< 0.1%
4 2
 
< 0.1%
5 2
 
< 0.1%
6 3
 
< 0.1%
8 1
 
< 0.1%
10 1
 
< 0.1%
12 1
 
< 0.1%
15 1
 
< 0.1%
16 1
 
< 0.1%
ValueCountFrequency (%)
42868148 1
< 0.1%
38648568 1
< 0.1%
38102204 1
< 0.1%
37629848 1
< 0.1%
29354052 1
< 0.1%
28893496 1
< 0.1%
27219772 1
< 0.1%
25510316 1
< 0.1%
25118900 1
< 0.1%
23306064 1
< 0.1%

SEC_NO_OF_ACCTS
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0664
Minimum0
Maximum38
Zeros9738
Zeros (%)97.4%
Negative0
Negative (%)0.0%
Memory size156.2 KiB
2025-03-05T20:50:29.211163image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum38
Range38
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.7341968
Coefficient of variation (CV)11.057181
Kurtosis1154.1338
Mean0.0664
Median Absolute Deviation (MAD)0
Skewness28.480334
Sum664
Variance0.53904494
MonotonicityNot monotonic
2025-03-05T20:50:29.968381image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 9738
97.4%
1 143
 
1.4%
2 57
 
0.6%
3 20
 
0.2%
4 17
 
0.2%
8 6
 
0.1%
6 4
 
< 0.1%
11 3
 
< 0.1%
5 2
 
< 0.1%
9 2
 
< 0.1%
Other values (7) 8
 
0.1%
ValueCountFrequency (%)
0 9738
97.4%
1 143
 
1.4%
2 57
 
0.6%
3 20
 
0.2%
4 17
 
0.2%
5 2
 
< 0.1%
6 4
 
< 0.1%
7 1
 
< 0.1%
8 6
 
0.1%
9 2
 
< 0.1%
ValueCountFrequency (%)
38 1
 
< 0.1%
31 1
 
< 0.1%
19 1
 
< 0.1%
18 1
 
< 0.1%
13 1
 
< 0.1%
11 3
< 0.1%
10 2
 
< 0.1%
9 2
 
< 0.1%
8 6
0.1%
7 1
 
< 0.1%

SEC_ACTIVE_ACCTS
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0306
Minimum0
Maximum14
Zeros9822
Zeros (%)98.2%
Negative0
Negative (%)0.0%
Memory size156.2 KiB
2025-03-05T20:50:30.662668image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum14
Range14
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.32783287
Coefficient of variation (CV)10.713492
Kurtosis702.51966
Mean0.0306
Median Absolute Deviation (MAD)0
Skewness22.408786
Sum306
Variance0.10747439
MonotonicityNot monotonic
2025-03-05T20:50:30.892070image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 9822
98.2%
1 122
 
1.2%
2 33
 
0.3%
3 11
 
0.1%
4 5
 
0.1%
11 2
 
< 0.1%
8 2
 
< 0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
14 1
 
< 0.1%
ValueCountFrequency (%)
0 9822
98.2%
1 122
 
1.2%
2 33
 
0.3%
3 11
 
0.1%
4 5
 
0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
8 2
 
< 0.1%
11 2
 
< 0.1%
14 1
 
< 0.1%
ValueCountFrequency (%)
14 1
 
< 0.1%
11 2
 
< 0.1%
8 2
 
< 0.1%
7 1
 
< 0.1%
6 1
 
< 0.1%
4 5
 
0.1%
3 11
 
0.1%
2 33
 
0.3%
1 122
 
1.2%
0 9822
98.2%

SEC_OVERDUE_ACCTS
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0098
Minimum0
Maximum6
Zeros9927
Zeros (%)99.3%
Negative0
Negative (%)0.0%
Memory size156.2 KiB
2025-03-05T20:50:31.099474image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.14179552
Coefficient of variation (CV)14.468931
Kurtosis759.24546
Mean0.0098
Median Absolute Deviation (MAD)0
Skewness23.861775
Sum98
Variance0.020105971
MonotonicityNot monotonic
2025-03-05T20:50:31.303043image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 9927
99.3%
1 62
 
0.6%
2 5
 
0.1%
5 2
 
< 0.1%
3 2
 
< 0.1%
4 1
 
< 0.1%
6 1
 
< 0.1%
ValueCountFrequency (%)
0 9927
99.3%
1 62
 
0.6%
2 5
 
0.1%
3 2
 
< 0.1%
4 1
 
< 0.1%
5 2
 
< 0.1%
6 1
 
< 0.1%
ValueCountFrequency (%)
6 1
 
< 0.1%
5 2
 
< 0.1%
4 1
 
< 0.1%
3 2
 
< 0.1%
2 5
 
0.1%
1 62
 
0.6%
0 9927
99.3%

SEC_CURRENT_BALANCE
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct150
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7049.7148
Minimum0
Maximum10779261
Zeros9850
Zeros (%)98.5%
Negative0
Negative (%)0.0%
Memory size156.2 KiB
2025-03-05T20:50:31.619574image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum10779261
Range10779261
Interquartile range (IQR)0

Descriptive statistics

Standard deviation194151.86
Coefficient of variation (CV)27.540385
Kurtosis2433.2679
Mean7049.7148
Median Absolute Deviation (MAD)0
Skewness46.914077
Sum70497148
Variance3.7694944 × 1010
MonotonicityNot monotonic
2025-03-05T20:50:32.270647image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9850
98.5%
27296 2
 
< 0.1%
210646 1
 
< 0.1%
541134 1
 
< 0.1%
137353 1
 
< 0.1%
1900000 1
 
< 0.1%
54093 1
 
< 0.1%
1180016 1
 
< 0.1%
5162 1
 
< 0.1%
1376603 1
 
< 0.1%
Other values (140) 140
 
1.4%
ValueCountFrequency (%)
0 9850
98.5%
99 1
 
< 0.1%
100 1
 
< 0.1%
183 1
 
< 0.1%
279 1
 
< 0.1%
608 1
 
< 0.1%
800 1
 
< 0.1%
1358 1
 
< 0.1%
1687 1
 
< 0.1%
1722 1
 
< 0.1%
ValueCountFrequency (%)
10779261 1
< 0.1%
10716039 1
< 0.1%
9328157 1
< 0.1%
3618737 1
< 0.1%
3558071 1
< 0.1%
2524747 1
< 0.1%
2067601 1
< 0.1%
1900000 1
< 0.1%
1651016 1
< 0.1%
1463930 1
< 0.1%

SEC_SANCTIONED_AMOUNT
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct150
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9512.7601
Minimum0
Maximum11900000
Zeros9824
Zeros (%)98.2%
Negative0
Negative (%)0.0%
Memory size156.2 KiB
2025-03-05T20:50:32.950749image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum11900000
Range11900000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation223043.16
Coefficient of variation (CV)23.446734
Kurtosis2100.0337
Mean9512.7601
Median Absolute Deviation (MAD)0
Skewness42.776525
Sum95127601
Variance4.9748251 × 1010
MonotonicityNot monotonic
2025-03-05T20:50:33.257103image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9824
98.2%
50000 7
 
0.1%
40000 4
 
< 0.1%
43000 4
 
< 0.1%
29000 3
 
< 0.1%
57000 3
 
< 0.1%
35000 2
 
< 0.1%
200000 2
 
< 0.1%
68000 2
 
< 0.1%
500000 2
 
< 0.1%
Other values (140) 147
 
1.5%
ValueCountFrequency (%)
0 9824
98.2%
82 1
 
< 0.1%
99 1
 
< 0.1%
250 1
 
< 0.1%
346 1
 
< 0.1%
771 1
 
< 0.1%
1358 1
 
< 0.1%
3051 1
 
< 0.1%
5751 1
 
< 0.1%
6343 1
 
< 0.1%
ValueCountFrequency (%)
11900000 1
< 0.1%
11365790 1
< 0.1%
11000000 1
< 0.1%
3953133 1
< 0.1%
3503003 1
< 0.1%
3183000 1
< 0.1%
3098146 1
< 0.1%
2796000 1
< 0.1%
2200000 1
< 0.1%
2100000 1
< 0.1%

SEC_DISBURSED_AMOUNT
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct158
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9433.451
Minimum0
Maximum11900000
Zeros9826
Zeros (%)98.3%
Negative0
Negative (%)0.0%
Memory size156.2 KiB
2025-03-05T20:50:33.606966image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum11900000
Range11900000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation222742.93
Coefficient of variation (CV)23.61203
Kurtosis2110.6008
Mean9433.451
Median Absolute Deviation (MAD)0
Skewness42.902369
Sum94334510
Variance4.9614414 × 1010
MonotonicityNot monotonic
2025-03-05T20:50:34.077083image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9826
98.3%
50000 4
 
< 0.1%
43000 4
 
< 0.1%
40000 3
 
< 0.1%
29000 3
 
< 0.1%
48000 2
 
< 0.1%
17050 2
 
< 0.1%
500000 2
 
< 0.1%
2000000 2
 
< 0.1%
200000 2
 
< 0.1%
Other values (148) 150
 
1.5%
ValueCountFrequency (%)
0 9826
98.3%
82 1
 
< 0.1%
99 1
 
< 0.1%
250 1
 
< 0.1%
346 1
 
< 0.1%
479 1
 
< 0.1%
771 1
 
< 0.1%
1100 1
 
< 0.1%
1358 1
 
< 0.1%
3051 1
 
< 0.1%
ValueCountFrequency (%)
11900000 1
< 0.1%
11365790 1
< 0.1%
11000000 1
< 0.1%
3953133 1
< 0.1%
3503003 1
< 0.1%
3115312 1
< 0.1%
2993739 1
< 0.1%
2796000 1
< 0.1%
2200000 1
< 0.1%
2100000 1
< 0.1%

PRIMARY_INSTAL_AMT
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct2789
Distinct (%)27.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12425.418
Minimum0
Maximum5718114
Zeros6776
Zeros (%)67.8%
Negative0
Negative (%)0.0%
Memory size156.2 KiB
2025-03-05T20:50:34.426341image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32091.75
95-th percentile27521.15
Maximum5718114
Range5718114
Interquartile range (IQR)2091.75

Descriptive statistics

Standard deviation109509.43
Coefficient of variation (CV)8.8133395
Kurtosis1242.6205
Mean12425.418
Median Absolute Deviation (MAD)0
Skewness29.580561
Sum1.2425418 × 108
Variance1.1992315 × 1010
MonotonicityNot monotonic
2025-03-05T20:50:34.905394image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6776
67.8%
1620 12
 
0.1%
2000 9
 
0.1%
1350 7
 
0.1%
1700 6
 
0.1%
1399 6
 
0.1%
1250 6
 
0.1%
1600 6
 
0.1%
1900 5
 
0.1%
1000 5
 
0.1%
Other values (2779) 3162
31.6%
ValueCountFrequency (%)
0 6776
67.8%
1 1
 
< 0.1%
3 1
 
< 0.1%
7 1
 
< 0.1%
10 2
 
< 0.1%
13 1
 
< 0.1%
20 1
 
< 0.1%
21 2
 
< 0.1%
23 1
 
< 0.1%
25 1
 
< 0.1%
ValueCountFrequency (%)
5718114 1
< 0.1%
4897837 1
< 0.1%
2614000 1
< 0.1%
2552650 1
< 0.1%
2082392 1
< 0.1%
1913555 1
< 0.1%
1772411 1
< 0.1%
1641779 1
< 0.1%
1500510 1
< 0.1%
1396000 1
< 0.1%

SEC_INSTAL_AMT
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct106
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean230.0842
Minimum0
Maximum280000
Zeros9895
Zeros (%)99.0%
Negative0
Negative (%)0.0%
Memory size156.2 KiB
2025-03-05T20:50:35.556430image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum280000
Range280000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5467.9226
Coefficient of variation (CV)23.764876
Kurtosis1345.7345
Mean230.0842
Median Absolute Deviation (MAD)0
Skewness34.505021
Sum2300842
Variance29898177
MonotonicityNot monotonic
2025-03-05T20:50:36.624248image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9895
99.0%
1404 1
 
< 0.1%
1046 1
 
< 0.1%
2990 1
 
< 0.1%
23528 1
 
< 0.1%
14611 1
 
< 0.1%
2270 1
 
< 0.1%
8338 1
 
< 0.1%
253 1
 
< 0.1%
1258 1
 
< 0.1%
Other values (96) 96
 
1.0%
ValueCountFrequency (%)
0 9895
99.0%
66 1
 
< 0.1%
231 1
 
< 0.1%
253 1
 
< 0.1%
784 1
 
< 0.1%
833 1
 
< 0.1%
1025 1
 
< 0.1%
1046 1
 
< 0.1%
1100 1
 
< 0.1%
1166 1
 
< 0.1%
ValueCountFrequency (%)
280000 1
< 0.1%
219214 1
< 0.1%
181000 1
< 0.1%
162882 1
< 0.1%
160569 1
< 0.1%
146920 1
< 0.1%
135000 1
< 0.1%
114000 1
< 0.1%
102999 1
< 0.1%
99235 1
< 0.1%

NEW_ACCTS_IN_LAST_SIX_MONTHS
Real number (ℝ)

High correlation  Zeros 

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3886
Minimum0
Maximum14
Zeros7737
Zeros (%)77.4%
Negative0
Negative (%)0.0%
Memory size156.2 KiB
2025-03-05T20:50:36.942444image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum14
Range14
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.94576926
Coefficient of variation (CV)2.433786
Kurtosis25.202723
Mean0.3886
Median Absolute Deviation (MAD)0
Skewness4.0716172
Sum3886
Variance0.89447949
MonotonicityNot monotonic
2025-03-05T20:50:37.624910image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 7737
77.4%
1 1414
 
14.1%
2 477
 
4.8%
3 185
 
1.8%
4 92
 
0.9%
5 41
 
0.4%
6 24
 
0.2%
7 14
 
0.1%
8 8
 
0.1%
9 3
 
< 0.1%
Other values (3) 5
 
0.1%
ValueCountFrequency (%)
0 7737
77.4%
1 1414
 
14.1%
2 477
 
4.8%
3 185
 
1.8%
4 92
 
0.9%
5 41
 
0.4%
6 24
 
0.2%
7 14
 
0.1%
8 8
 
0.1%
9 3
 
< 0.1%
ValueCountFrequency (%)
14 1
 
< 0.1%
13 1
 
< 0.1%
10 3
 
< 0.1%
9 3
 
< 0.1%
8 8
 
0.1%
7 14
 
0.1%
6 24
 
0.2%
5 41
 
0.4%
4 92
0.9%
3 185
1.8%

DELINQUENT_ACCTS_IN_LAST_SIX_MONTHS
Real number (ℝ)

Zeros 

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1044
Minimum0
Maximum11
Zeros9182
Zeros (%)91.8%
Negative0
Negative (%)0.0%
Memory size156.2 KiB
2025-03-05T20:50:37.877853image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum11
Range11
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.41655491
Coefficient of variation (CV)3.9899895
Kurtosis105.54096
Mean0.1044
Median Absolute Deviation (MAD)0
Skewness7.5025747
Sum1044
Variance0.17351799
MonotonicityNot monotonic
2025-03-05T20:50:38.101664image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 9182
91.8%
1 662
 
6.6%
2 126
 
1.3%
3 16
 
0.2%
4 5
 
0.1%
7 3
 
< 0.1%
6 2
 
< 0.1%
5 2
 
< 0.1%
8 1
 
< 0.1%
11 1
 
< 0.1%
ValueCountFrequency (%)
0 9182
91.8%
1 662
 
6.6%
2 126
 
1.3%
3 16
 
0.2%
4 5
 
0.1%
5 2
 
< 0.1%
6 2
 
< 0.1%
7 3
 
< 0.1%
8 1
 
< 0.1%
11 1
 
< 0.1%
ValueCountFrequency (%)
11 1
 
< 0.1%
8 1
 
< 0.1%
7 3
 
< 0.1%
6 2
 
< 0.1%
5 2
 
< 0.1%
4 5
 
0.1%
3 16
 
0.2%
2 126
 
1.3%
1 662
 
6.6%
0 9182
91.8%
Distinct112
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2025-03-05T20:50:38.467645image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length9.0769
Min length9

Characters and Unicode

Total characters90769
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)0.2%

Sample

1st row0yrs 0mon
2nd row0yrs 0mon
3rd row0yrs 5mon
4th row0yrs 0mon
5th row0yrs 0mon
ValueCountFrequency (%)
0yrs 7188
35.9%
0mon 5467
27.3%
1yrs 1584
 
7.9%
2yrs 672
 
3.4%
6mon 466
 
2.3%
1mon 453
 
2.3%
4mon 429
 
2.1%
5mon 420
 
2.1%
7mon 419
 
2.1%
2mon 415
 
2.1%
Other values (16) 2487
 
12.4%
2025-03-05T20:50:39.217806image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 13050
14.4%
r 10000
11.0%
s 10000
11.0%
10000
11.0%
m 10000
11.0%
o 10000
11.0%
n 10000
11.0%
y 10000
11.0%
1 3176
 
3.5%
2 1090
 
1.2%
Other values (7) 3453
 
3.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 90769
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 13050
14.4%
r 10000
11.0%
s 10000
11.0%
10000
11.0%
m 10000
11.0%
o 10000
11.0%
n 10000
11.0%
y 10000
11.0%
1 3176
 
3.5%
2 1090
 
1.2%
Other values (7) 3453
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 90769
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 13050
14.4%
r 10000
11.0%
s 10000
11.0%
10000
11.0%
m 10000
11.0%
o 10000
11.0%
n 10000
11.0%
y 10000
11.0%
1 3176
 
3.5%
2 1090
 
1.2%
Other values (7) 3453
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 90769
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 13050
14.4%
r 10000
11.0%
s 10000
11.0%
10000
11.0%
m 10000
11.0%
o 10000
11.0%
n 10000
11.0%
y 10000
11.0%
1 3176
 
3.5%
2 1090
 
1.2%
Other values (7) 3453
 
3.8%
Distinct187
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2025-03-05T20:50:39.570304image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length11
Median length9
Mean length9.0894
Min length9

Characters and Unicode

Total characters90894
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique31 ?
Unique (%)0.3%

Sample

1st row0yrs 0mon
2nd row0yrs 0mon
3rd row0yrs 5mon
4th row0yrs 0mon
5th row0yrs 0mon
ValueCountFrequency (%)
0yrs 6280
31.4%
0mon 5582
27.9%
1yrs 1201
 
6.0%
2yrs 957
 
4.8%
1mon 613
 
3.1%
3yrs 505
 
2.5%
6mon 495
 
2.5%
7mon 404
 
2.0%
2mon 395
 
2.0%
3mon 384
 
1.9%
Other values (23) 3184
15.9%
2025-03-05T20:50:41.235650image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 12254
13.5%
r 10000
11.0%
s 10000
11.0%
10000
11.0%
m 10000
11.0%
o 10000
11.0%
n 10000
11.0%
y 10000
11.0%
1 3126
 
3.4%
2 1390
 
1.5%
Other values (7) 4124
 
4.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 90894
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 12254
13.5%
r 10000
11.0%
s 10000
11.0%
10000
11.0%
m 10000
11.0%
o 10000
11.0%
n 10000
11.0%
y 10000
11.0%
1 3126
 
3.4%
2 1390
 
1.5%
Other values (7) 4124
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 90894
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 12254
13.5%
r 10000
11.0%
s 10000
11.0%
10000
11.0%
m 10000
11.0%
o 10000
11.0%
n 10000
11.0%
y 10000
11.0%
1 3126
 
3.4%
2 1390
 
1.5%
Other values (7) 4124
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 90894
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 12254
13.5%
r 10000
11.0%
s 10000
11.0%
10000
11.0%
m 10000
11.0%
o 10000
11.0%
n 10000
11.0%
y 10000
11.0%
1 3126
 
3.4%
2 1390
 
1.5%
Other values (7) 4124
 
4.5%

NO_OF_INQUIRIES
Real number (ℝ)

Zeros 

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2133
Minimum0
Maximum18
Zeros8624
Zeros (%)86.2%
Negative0
Negative (%)0.0%
Memory size156.2 KiB
2025-03-05T20:50:41.934607image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum18
Range18
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.73174904
Coefficient of variation (CV)3.4306097
Kurtosis112.01559
Mean0.2133
Median Absolute Deviation (MAD)0
Skewness7.8998531
Sum2133
Variance0.53545666
MonotonicityNot monotonic
2025-03-05T20:50:42.847310image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 8624
86.2%
1 989
 
9.9%
2 236
 
2.4%
3 70
 
0.7%
4 37
 
0.4%
5 18
 
0.2%
6 10
 
0.1%
8 4
 
< 0.1%
9 3
 
< 0.1%
10 2
 
< 0.1%
Other values (6) 7
 
0.1%
ValueCountFrequency (%)
0 8624
86.2%
1 989
 
9.9%
2 236
 
2.4%
3 70
 
0.7%
4 37
 
0.4%
5 18
 
0.2%
6 10
 
0.1%
7 2
 
< 0.1%
8 4
 
< 0.1%
9 3
 
< 0.1%
ValueCountFrequency (%)
18 1
 
< 0.1%
17 1
 
< 0.1%
13 1
 
< 0.1%
12 1
 
< 0.1%
11 1
 
< 0.1%
10 2
 
< 0.1%
9 3
 
< 0.1%
8 4
 
< 0.1%
7 2
 
< 0.1%
6 10
0.1%

LOAN_DEFAULT
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
6746 
1
3254 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 6746
67.5%
1 3254
32.5%

Length

2025-03-05T20:50:43.591768image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-05T20:50:43.904633image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 6746
67.5%
1 3254
32.5%

Most occurring characters

ValueCountFrequency (%)
0 6746
67.5%
1 3254
32.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 6746
67.5%
1 3254
32.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 6746
67.5%
1 3254
32.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 6746
67.5%
1 3254
32.5%

Interactions

2025-03-05T20:49:48.154797image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:44:25.618290image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:44:37.443784image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:44:51.611299image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:45:10.324145image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:45:24.108517image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:45:37.384843image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:45:51.426884image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:46:01.871809image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:46:13.396321image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:46:26.467631image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:46:40.305828image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:46:53.314340image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:47:05.099132image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:47:13.933145image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:47:23.941881image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:47:32.294959image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:47:40.320905image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:47:51.143506image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:48:04.182922image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:48:16.496607image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:48:28.593701image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:48:39.306461image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:48:52.332722image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:49:06.222900image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:49:17.246444image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:49:26.905202image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:49:36.187455image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:49:48.632889image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:44:25.978633image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:44:38.257707image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:44:52.134895image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:45:10.860635image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:45:24.398128image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:45:37.808537image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2025-03-05T20:48:01.744679image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:48:14.208359image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:48:25.876281image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:48:36.473285image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:48:49.778855image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:49:04.585299image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:49:15.060672image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:49:23.683111image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:49:33.791151image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:49:45.992461image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:49:56.109930image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:44:34.357472image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:44:48.645747image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:45:05.950557image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:45:22.755633image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:45:35.093947image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:45:49.284615image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:45:59.739696image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:46:11.235767image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:46:23.903370image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:46:38.236652image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:46:51.528719image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:47:03.335809image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:47:12.632894image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:47:22.439484image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:47:31.170538image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:47:38.577357image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:47:48.414596image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:48:02.245298image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:48:14.615165image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:48:26.093117image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:48:36.884025image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:48:50.141246image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:49:04.868691image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:49:15.561813image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:49:24.246865image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:49:34.191582image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:49:46.412135image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:49:56.825296image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:44:34.702611image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:44:49.207745image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:45:06.682764image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:45:23.023092image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:45:35.498017image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:45:49.691602image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:46:00.754272image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:46:11.648513image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:46:24.328435image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:46:38.516320image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:46:51.896969image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:47:03.859172image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:47:12.872070image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:47:22.766097image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:47:31.374241image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:47:39.069272image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:47:48.820436image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:48:02.648067image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:48:15.009184image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:48:26.773192image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:48:37.200474image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:48:50.574729image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:49:05.201203image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:49:15.867343image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:49:25.044169image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:49:34.862752image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:49:46.739732image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:49:57.063044image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:44:35.005052image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:44:49.584604image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:45:07.371250image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:45:23.308785image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:45:35.888819image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:45:50.010758image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:46:01.174170image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:46:12.058215image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:46:24.785265image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:46:38.962229image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:46:52.273455image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:47:04.097902image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:47:13.081997image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:47:23.013751image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:47:31.584247image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:47:39.571554image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:47:49.413632image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:48:03.013210image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:48:15.342459image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:48:27.777575image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:48:37.565244image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:48:50.943589image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:49:05.451860image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:49:16.165228image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:49:25.785023image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:49:35.287947image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:49:47.060868image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:49:58.111309image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:44:35.524750image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:44:50.278799image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:45:08.024284image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:45:23.647655image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:45:36.400194image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:45:50.331152image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:46:01.430769image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:46:12.537997image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:46:25.601897image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:46:39.404326image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:46:52.688110image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:47:04.385453image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:47:13.363835image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:47:23.269853image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:47:31.795516image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:47:39.863082image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:47:49.825624image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:48:03.397348image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:48:15.715754image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:48:28.048584image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:48:38.152937image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:48:51.346912image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:49:05.672046image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:49:16.396254image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:49:26.388859image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:49:35.622353image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:49:47.478242image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:49:58.482213image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:44:36.496776image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:44:50.952296image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:45:09.192719image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:45:23.884873image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:45:36.821170image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:45:51.025164image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:46:01.625219image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:46:12.883406image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:46:26.015360image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:46:39.878371image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:46:53.030202image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:47:04.839500image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:47:13.653546image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:47:23.486462image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:47:32.019592image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:47:40.068830image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:47:50.557667image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:48:03.794290image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:48:16.070613image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:48:28.299076image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:48:38.739710image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:48:51.943286image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:49:05.891145image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:49:16.872041image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:49:26.670450image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:49:35.891675image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-05T20:49:47.825182image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-03-05T20:50:44.302258image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
AADHAR_FLAGASSET_COSTBRANCH_IDCURRENT_PINCODE_IDDELINQUENT_ACCTS_IN_LAST_SIX_MONTHSDISBURSED_AMOUNTDRIVING_FLAGEMPLOYEE_CODE_IDEMPLOYMENT_TYPELOAN_DEFAULTLTVMANUFACTURER_IDNEW_ACCTS_IN_LAST_SIX_MONTHSNO_OF_INQUIRIESPAN_FLAGPASSPORT_FLAGPERFORM_CNS_SCOREPERFORM_CNS_SCORE_DESCRIPTIONPRIMARY_INSTAL_AMTPRI_ACTIVE_ACCTSPRI_CURRENT_BALANCEPRI_DISBURSED_AMOUNTPRI_NO_OF_ACCTSPRI_OVERDUE_ACCTSPRI_SANCTIONED_AMOUNTSEC_ACTIVE_ACCTSSEC_CURRENT_BALANCESEC_DISBURSED_AMOUNTSEC_INSTAL_AMTSEC_NO_OF_ACCTSSEC_OVERDUE_ACCTSSEC_SANCTIONED_AMOUNTSTATE_IDSUPPLIER_IDUNIQUEIDVOTERID_FLAG
AADHAR_FLAG1.0000.0840.3270.3900.0000.0140.2920.1200.0960.0600.0990.0640.0460.0000.2050.0620.0650.0750.0000.0630.0090.0000.0380.0000.0000.0000.0000.0000.0000.0000.0000.0000.5370.1100.0210.868
ASSET_COST0.0841.000-0.0420.381-0.0420.6770.0060.0060.0460.027-0.2750.177-0.053-0.0430.0220.000-0.0710.019-0.071-0.053-0.050-0.052-0.069-0.021-0.051-0.036-0.038-0.038-0.037-0.046-0.039-0.038-0.0410.1660.2560.072
BRANCH_ID0.327-0.0421.0000.0700.015-0.0270.0430.0890.1530.070-0.008-0.0620.008-0.0100.1240.000-0.0080.043-0.0050.0170.0240.0160.0020.0130.017-0.008-0.006-0.007-0.013-0.007-0.017-0.0070.2510.193-0.0100.314
CURRENT_PINCODE_ID0.3900.3810.0701.000-0.0670.1050.079-0.0340.2730.143-0.297-0.060-0.117-0.1090.2440.046-0.1650.080-0.124-0.140-0.118-0.125-0.162-0.076-0.125-0.062-0.061-0.062-0.047-0.072-0.049-0.0620.0600.1930.0310.373
DELINQUENT_ACCTS_IN_LAST_SIX_MONTHS0.000-0.0420.015-0.0671.000-0.0040.000-0.0060.0100.0240.0470.0180.2070.0720.0000.0000.1860.1090.3190.3650.3590.3640.3510.4570.3640.1070.0960.1040.0780.1120.1090.1060.007-0.023-0.0050.008
DISBURSED_AMOUNT0.0140.677-0.0270.105-0.0041.0000.0250.0140.0470.1040.4130.1800.0220.0430.0230.000-0.0030.0260.0150.0260.0190.0160.0200.0220.015-0.021-0.022-0.024-0.016-0.018-0.020-0.023-0.0040.0630.1700.000
DRIVING_FLAG0.2920.0060.0430.0790.0000.0251.0000.0060.0000.0120.0000.0000.0000.0000.0000.0000.0250.0160.0000.0600.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0940.0310.0150.055
EMPLOYEE_CODE_ID0.1200.0060.089-0.034-0.0060.0140.0061.0000.0540.0250.004-0.035-0.006-0.0100.0690.036-0.0190.011-0.016-0.0020.002-0.000-0.005-0.007-0.001-0.018-0.012-0.0200.001-0.013-0.020-0.0190.1040.1220.0660.114
EMPLOYMENT_TYPE0.0960.0460.1530.2730.0100.0470.0000.0541.0000.3340.1220.0740.0220.0110.0130.0000.0660.0650.0200.0000.0240.0340.0100.0000.0350.0000.0000.0000.0210.0130.0110.0000.2280.1120.0550.089
LOAN_DEFAULT0.0600.0270.0700.1430.0240.1040.0120.0250.3341.0000.1040.0390.1200.0000.0000.0120.4340.4330.0170.1010.0000.0070.0640.0400.0080.0000.0140.0090.0000.0000.0040.0090.1100.0380.0270.054
LTV0.099-0.275-0.008-0.2970.0470.4130.0000.0040.1220.1041.0000.1010.0980.1060.0460.0000.0970.0410.1210.1100.0950.0930.1250.0640.0910.0130.0110.0120.0210.0270.0110.0130.021-0.145-0.0340.089
MANUFACTURER_ID0.0640.177-0.062-0.0600.0180.1800.000-0.0350.0740.0390.1011.0000.0550.0120.0450.0380.0910.0500.0810.0950.0850.0880.1070.0320.0870.0090.0050.0070.0110.024-0.0060.0080.059-0.044-0.0310.071
NEW_ACCTS_IN_LAST_SIX_MONTHS0.046-0.0530.008-0.1170.2070.0220.000-0.0060.0220.1200.0980.0551.0000.3040.0000.0000.4590.1470.4660.6870.6290.6020.6100.1540.6030.1060.0970.1030.0780.1080.0430.1030.005-0.0680.0010.046
NO_OF_INQUIRIES0.000-0.043-0.010-0.1090.0720.0430.000-0.0100.0110.0000.1060.0120.3041.0000.0210.0000.1880.0650.2790.2460.2160.2030.2420.1030.2020.0080.0080.009-0.0020.012-0.0040.008-0.015-0.0700.0100.000
PAN_FLAG0.2050.0220.1240.2440.0000.0230.0000.0690.0130.0000.0460.0450.0000.0211.0000.0000.0270.0000.0000.0080.0000.0000.0220.0000.0000.0400.0110.0050.0290.0460.0120.0050.2720.0720.0260.177
PASSPORT_FLAG0.0620.0000.0000.0460.0000.0000.0000.0360.0000.0120.0000.0380.0000.0000.0001.0000.0280.0280.0000.0000.0000.0000.0100.1120.0000.0000.0000.0000.0000.0000.0000.0000.0000.0220.0000.015
PERFORM_CNS_SCORE0.065-0.071-0.008-0.1650.186-0.0030.025-0.0190.0660.4340.0970.0910.4590.1880.0270.0281.0000.8930.6190.7010.6490.6870.8550.1650.6890.0720.0690.0720.0650.0950.0440.072-0.029-0.098-0.0000.076
PERFORM_CNS_SCORE_DESCRIPTION0.0750.0190.0430.0800.1090.0260.0160.0110.0650.4330.0410.0500.1470.0650.0000.0280.8931.0000.0530.1500.0660.0730.1280.1350.0730.0370.0760.0460.0380.0250.0240.0460.0710.0340.0310.081
PRIMARY_INSTAL_AMT0.000-0.071-0.005-0.1240.3190.0150.000-0.0160.0200.0170.1210.0810.4660.2790.0000.0000.6190.0531.0000.6610.6620.6620.7330.3390.6570.0630.0530.0590.0650.0940.0400.0610.000-0.075-0.0080.000
PRI_ACTIVE_ACCTS0.063-0.0530.017-0.1400.3650.0260.060-0.0020.0000.1010.1100.0950.6870.2460.0080.0000.7010.1500.6611.0000.9240.9550.8860.4190.9550.0890.0780.0860.0660.1170.0480.087-0.008-0.079-0.0090.059
PRI_CURRENT_BALANCE0.009-0.0500.024-0.1180.3590.0190.0000.0020.0240.0000.0950.0850.6290.2160.0000.0000.6490.0660.6620.9241.0000.9600.8200.4210.9550.0760.0690.0730.0580.1060.0410.0750.003-0.0540.0020.017
PRI_DISBURSED_AMOUNT0.000-0.0520.016-0.1250.3640.0160.000-0.0000.0340.0070.0930.0880.6020.2030.0000.0000.6870.0730.6620.9550.9601.0000.8480.4220.9950.0800.0700.0770.0590.1090.0420.078-0.002-0.065-0.0070.009
PRI_NO_OF_ACCTS0.038-0.0690.002-0.1620.3510.0200.000-0.0050.0100.0640.1250.1070.6100.2420.0220.0100.8550.1280.7330.8860.8200.8481.0000.4150.8480.1000.0900.0970.0820.1340.0590.098-0.010-0.104-0.0010.039
PRI_OVERDUE_ACCTS0.000-0.0210.013-0.0760.4570.0220.000-0.0070.0000.0400.0640.0320.1540.1030.0000.1120.1650.1350.3390.4190.4210.4220.4151.0000.4190.0690.0520.0640.0340.0860.0360.065-0.003-0.0450.0010.000
PRI_SANCTIONED_AMOUNT0.000-0.0510.017-0.1250.3640.0150.000-0.0010.0350.0080.0910.0870.6030.2020.0000.0000.6890.0730.6570.9550.9550.9950.8480.4191.0000.0810.0710.0780.0590.1100.0420.079-0.003-0.065-0.0090.010
SEC_ACTIVE_ACCTS0.000-0.036-0.008-0.0620.107-0.0210.000-0.0180.0000.0000.0130.0090.1060.0080.0400.0000.0720.0370.0630.0890.0760.0800.1000.0690.0811.0000.9170.9880.5960.8230.5140.9940.024-0.048-0.0590.000
SEC_CURRENT_BALANCE0.000-0.038-0.006-0.0610.096-0.0220.000-0.0120.0000.0140.0110.0050.0970.0080.0110.0000.0690.0760.0530.0780.0690.0700.0900.0520.0710.9171.0000.9290.6010.7550.5600.9230.022-0.045-0.0600.013
SEC_DISBURSED_AMOUNT0.000-0.038-0.007-0.0620.104-0.0240.000-0.0200.0000.0090.0120.0070.1030.0090.0050.0000.0720.0460.0590.0860.0730.0770.0970.0640.0780.9880.9291.0000.6020.8130.5200.9940.022-0.049-0.0580.000
SEC_INSTAL_AMT0.000-0.037-0.013-0.0470.078-0.0160.0000.0010.0210.0000.0210.0110.078-0.0020.0290.0000.0650.0380.0650.0660.0580.0590.0820.0340.0590.5960.6010.6021.0000.6310.3950.5980.024-0.054-0.0450.000
SEC_NO_OF_ACCTS0.000-0.046-0.007-0.0720.112-0.0180.000-0.0130.0130.0000.0270.0240.1080.0120.0460.0000.0950.0250.0940.1170.1060.1090.1340.0860.1100.8230.7550.8130.6311.0000.5250.8180.028-0.054-0.0750.000
SEC_OVERDUE_ACCTS0.000-0.039-0.017-0.0490.109-0.0200.000-0.0200.0110.0040.011-0.0060.043-0.0040.0120.0000.0440.0240.0400.0480.0410.0420.0590.0360.0420.5140.5600.5200.3950.5251.0000.5170.012-0.039-0.0480.000
SEC_SANCTIONED_AMOUNT0.000-0.038-0.007-0.0620.106-0.0230.000-0.0190.0000.0090.0130.0080.1030.0080.0050.0000.0720.0460.0610.0870.0750.0780.0980.0650.0790.9940.9230.9940.5980.8180.5171.0000.025-0.049-0.0580.000
STATE_ID0.537-0.0410.2510.0600.007-0.0040.0940.1040.2280.1100.0210.0590.005-0.0150.2720.000-0.0290.0710.000-0.0080.003-0.002-0.010-0.003-0.0030.0240.0220.0220.0240.0280.0120.0251.0000.092-0.0770.515
SUPPLIER_ID0.1100.1660.1930.193-0.0230.0630.0310.1220.1120.038-0.145-0.044-0.068-0.0700.0720.022-0.0980.034-0.075-0.079-0.054-0.065-0.104-0.045-0.065-0.048-0.045-0.049-0.054-0.054-0.039-0.0490.0921.0000.0320.110
UNIQUEID0.0210.256-0.0100.031-0.0050.1700.0150.0660.0550.027-0.034-0.0310.0010.0100.0260.000-0.0000.031-0.008-0.0090.002-0.007-0.0010.001-0.009-0.059-0.060-0.058-0.045-0.075-0.048-0.058-0.0770.0321.0000.016
VOTERID_FLAG0.8680.0720.3140.3730.0080.0000.0550.1140.0890.0540.0890.0710.0460.0000.1770.0150.0760.0810.0000.0590.0170.0090.0390.0000.0100.0000.0130.0000.0000.0000.0000.0000.5150.1100.0161.000

Missing values

2025-03-05T20:49:59.304461image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-05T20:50:01.944069image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

UNIQUEIDDISBURSED_AMOUNTASSET_COSTLTVBRANCH_IDSUPPLIER_IDMANUFACTURER_IDCURRENT_PINCODE_IDDATE_OF_BIRTHEMPLOYMENT_TYPEDISBURSAL_DATESTATE_IDEMPLOYEE_CODE_IDMOBILENO_AVL_FLAGAADHAR_FLAGPAN_FLAGVOTERID_FLAGDRIVING_FLAGPASSPORT_FLAGPERFORM_CNS_SCOREPERFORM_CNS_SCORE_DESCRIPTIONPRI_NO_OF_ACCTSPRI_ACTIVE_ACCTSPRI_OVERDUE_ACCTSPRI_CURRENT_BALANCEPRI_SANCTIONED_AMOUNTPRI_DISBURSED_AMOUNTSEC_NO_OF_ACCTSSEC_ACTIVE_ACCTSSEC_OVERDUE_ACCTSSEC_CURRENT_BALANCESEC_SANCTIONED_AMOUNTSEC_DISBURSED_AMOUNTPRIMARY_INSTAL_AMTSEC_INSTAL_AMTNEW_ACCTS_IN_LAST_SIX_MONTHSDELINQUENT_ACCTS_IN_LAST_SIX_MONTHSAVERAGE_ACCT_AGECREDIT_HISTORY_LENGTHNO_OF_INQUIRIESLOAN_DEFAULT
27628573637512806884476.9920140048663071984-03-06Salaried2018-10-1356741100000No Bureau History Available00000000000000000yrs 0mon0yrs 0mon00
116249539920597137255184.0820181108662791972-05-07Self employed2018-09-26512961100000No Bureau History Available00000000000000000yrs 0mon0yrs 0mon01
119467615203528186800783.8178188048624501976-01-01Salaried2018-10-2441357110000763B-Very Low Risk11024969990999000000000100yrs 5mon0yrs 5mon00
78738443321483496218880.403418006869971986-01-15Self employed2018-08-17619611100000No Bureau History Available00000000000000000yrs 0mon0yrs 0mon01
5957613497605478300074.5818172924826861988-01-01Self employed2018-10-2441551001000No Bureau History Available00000000000000000yrs 0mon0yrs 0mon00
8601580160413947593156.631185644561041989-01-01Self employed2018-10-1531237110000365K-High Risk441100338110697861069786000000134340121yrs 3mon2yrs 6mon01
63416621570424847009062.9036185208667521988-07-01Salaried2018-10-251311221011000No Bureau History Available00000000000000000yrs 0mon0yrs 0mon00
80328574997706679107079.991352200212015961988-05-15Salaried2018-10-1342128111000778B-Very Low Risk64016645593343582330000300000000101yrs 11mon3yrs 7mon00
46055426611547596430387.095143478633531989-06-20Salaried2018-08-0798051100000No Bureau History Available00000000000000000yrs 0mon0yrs 0mon01
102215050736751712440356.51362404412067241989-07-14Self employed2018-09-14132820101100790B-Very Low Risk33091711100001019500000000200yrs 9mon1yrs 8mon00
UNIQUEIDDISBURSED_AMOUNTASSET_COSTLTVBRANCH_IDSUPPLIER_IDMANUFACTURER_IDCURRENT_PINCODE_IDDATE_OF_BIRTHEMPLOYMENT_TYPEDISBURSAL_DATESTATE_IDEMPLOYEE_CODE_IDMOBILENO_AVL_FLAGAADHAR_FLAGPAN_FLAGVOTERID_FLAGDRIVING_FLAGPASSPORT_FLAGPERFORM_CNS_SCOREPERFORM_CNS_SCORE_DESCRIPTIONPRI_NO_OF_ACCTSPRI_ACTIVE_ACCTSPRI_OVERDUE_ACCTSPRI_CURRENT_BALANCEPRI_SANCTIONED_AMOUNTPRI_DISBURSED_AMOUNTSEC_NO_OF_ACCTSSEC_ACTIVE_ACCTSSEC_OVERDUE_ACCTSSEC_CURRENT_BALANCESEC_SANCTIONED_AMOUNTSEC_DISBURSED_AMOUNTPRIMARY_INSTAL_AMTSEC_INSTAL_AMTNEW_ACCTS_IN_LAST_SIX_MONTHSDELINQUENT_ACCTS_IN_LAST_SIX_MONTHSAVERAGE_ACCT_AGECREDIT_HISTORY_LENGTHNO_OF_INQUIRIESLOAN_DEFAULT
18960521247701239200077.172238528616511992-04-28Self employed2018-09-2041635110000692E-Low Risk220341268875000849871000000293480101yrs 0mon1yrs 10mon00
83834484368490497695064.98138141085133421982-10-30Salaried2018-08-3193219111000603H-Medium Risk41149157500005000000000023430121yrs 2mon2yrs 6mon00
125569570542443947248662.0820239454962241994-02-23Self employed2018-10-1151482110000836A-Very Low Risk10000000000000002yrs 1mon2yrs 1mon00
70903461837502536540079.4316220044530361974-01-01Salaried2018-08-24144391100000No Bureau History Available00000000000000000yrs 0mon0yrs 0mon00
126431626619519996779582.60103188238669761997-05-03Salaried2018-10-2671238110000825A-Very Low Risk20000000000048060010yrs 6mon0yrs 7mon00
72798569223586977399881.76105157988611921994-08-01Self employed2018-10-11634141100000No Bureau History Available00000000000000000yrs 0mon0yrs 0mon00
29833635227507837855865.5382185598648781971-05-25Self employed2018-10-29193344110000783B-Very Low Risk510163830000030000000000000002yrs 1mon3yrs 6mon00
55089528884535596781381.5577183974523411996-05-21Salaried2018-09-23426421100000No Bureau History Available00000000000000000yrs 0mon0yrs 0mon01
131096271814934910529147.499164504953681992-01-20Salaried2018-10-26322531100000No Bureau History Available00000000000000000yrs 0mon0yrs 0mon00
90677468415374396359660.5418148784527621983-05-03Self employed2018-08-28416961100000No Bureau History Available00000000000000000yrs 0mon0yrs 0mon01